{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Introduction_to_TFP.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L9_HzvxgSRPw"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_probability as tfp\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfd = tfp.distributions"
      ],
      "metadata": {
        "id": "qyrhYPS9SWix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normal = tfd.Normal(loc=0., scale=1.)"
      ],
      "metadata": {
        "id": "VTtSbqjYSaBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(normal)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNuNlBJqSo30",
        "outputId": "f0632248-8b87-4ea2-971d-ee9a755798c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tfp.distributions.Normal(\"Normal\", batch_shape=[], event_shape=[], dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_2 = tfd.Normal(loc=[0., 0.], scale=[1., 3.])"
      ],
      "metadata": {
        "id": "2LWckXXDSkxf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(normal_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hve0oy42TD4F",
        "outputId": "8bff5e43-68ee-4da2-956e-6524814de9fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tfp.distributions.Normal(\"Normal\", batch_shape=[2], event_shape=[], dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_2.sample()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9Si9GilTF-v",
        "outputId": "48ea834f-99fc-4269-d538-720a733f93c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2,), dtype=float32, numpy=array([-0.00851772, -3.3102    ], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_3 = tfd.MultivariateNormalDiag(loc = [[1.0, 0.3]])"
      ],
      "metadata": {
        "id": "YVNsI3qpU_jM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(normal_3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8O5PbqRW79q",
        "outputId": "eb1646cc-dc96-4fe4-ef09-1488d75ccc53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tfp.distributions.MultivariateNormalDiag(\"MultivariateNormalDiag\", batch_shape=[1], event_shape=[2], dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal.sample()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZmGUowbZjvI",
        "outputId": "d867e1e9-3eca-4ec4-c1e4-ef1b5b0f4205"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=-0.2907571>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_2.sample()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJkW2k-fX8Xj",
        "outputId": "53b3b662-a57f-4fd5-c781-107a8b234b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.646955 , 2.7479086], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_3.sample()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1dd70qKaC2Q",
        "outputId": "54bb3b04-f59c-471f-b2ae-e5e66ce5bb75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[2.3395805 , 0.72992957]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal.prob(0.4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wRvU5S7aJZo",
        "outputId": "35619a25-a5ac-441f-b8b4-0986776b4a5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.36827016>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_2.prob([0.4, 0.6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXrON804ak-Y",
        "outputId": "d755c7b5-353c-48ac-b069-b9d3e37bd450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.36827016, 0.13034755], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_3.prob([1, .4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWZLbTGrcDXb",
        "outputId": "7e03ab23-32e5-4148-cfdb-8c5c172916b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.15836117], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bernoulli Distributions"
      ],
      "metadata": {
        "id": "Y0Erwb_Hy4DZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_probability as tfp\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "\n",
        "tfd = tfp.distributions"
      ],
      "metadata": {
        "id": "IMkRFee0cXVH"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coin_flip = tfd.Bernoulli(probs=0.5, dtype=tf.int32)"
      ],
      "metadata": {
        "id": "u5HPh1T4y9qI"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coin_flip_data = coin_flip.sample(2000)"
      ],
      "metadata": {
        "id": "CNl8BQ0iUlY0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coin_flip.prob(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpsANHlwX_dR",
        "outputId": "be1ea7a6-0909-4b00-9488-fefe39609150"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.5>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coin_flip.log_prob(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDSr9ianYw9-",
        "outputId": "78f61171-868b-411a-af2d-d5a599a4701a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=-0.6931472>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(coin_flip_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "HzUl0JzyY9h7",
        "outputId": "37e6714c-592c-42c5-925c-d0071a71950a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1002.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "         999.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coin_flip.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qAWf9p2ZLDS",
        "outputId": "ba99c359-9d77-450a-ff98-153b9cfb94e2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.5>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coin_flip.stddev()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKoIP4qGZmn2",
        "outputId": "0ba657df-7bf3-4baa-e434-c8f6cf16dcae"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.5>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Biased coin"
      ],
      "metadata": {
        "id": "ZV7ndCYAcnpv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bias_coin_flip = tfd.Bernoulli(probs=0.8, dtype=tf.int32)\n",
        "bias_coin_flip_data = bias_coin_flip.sample(2000)\n",
        "plt.hist(bias_coin_flip_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "G560o_h2Zpgy",
        "outputId": "fc073bd0-955c-4772-baee-3446cdade73c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 370.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "        1630.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bias_coin_flip.prob(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EL6ADcF6cvhY",
        "outputId": "f8a5938d-6345-4806-d758-5bdad23ee063"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.19999999>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Two biased coins\n"
      ],
      "metadata": {
        "id": "Hv02wTOofR_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "two_bias_coins_flip = tfd.Bernoulli(probs=[0.8, 0.6], dtype=tf.int32)\n",
        "two_bias_coins_flip_data = two_bias_coins_flip.sample(2000)"
      ],
      "metadata": {
        "id": "twbL-wS6dK3D"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(two_bias_coins_flip_data[:,0], alpha=0.8, label='Coin 1')\n",
        "plt.hist(two_bias_coins_flip_data[:,1], alpha=0.5, label='Coin 2')\n",
        "plt.legend(loc='center')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "cJDn-1YXxGHP",
        "outputId": "6ce0068e-0e7c-403a-87ea-5a53ad19ebf6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f35d06fc6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Normal Distribution"
      ],
      "metadata": {
        "id": "sHVJlLQG1ALb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "ly0Bltec-m_-"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temperature = tfd.Normal(loc=35, scale = 4)"
      ],
      "metadata": {
        "id": "E_pCO5PsxXDE"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temperature_data = temperature.sample(1000)"
      ],
      "metadata": {
        "id": "20WEl3WR-PhP"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(temperature_data, kde= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "6C7VnK0j-XIU",
        "outputId": "29e84a1e-0132-4b86-d48e-8781dfe6a835"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f35d0530350>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.math.reduce_mean(temperature_data) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEG1k--2-bdo",
        "outputId": "5cc4c751-14dd-4221-f95f-0c3a225030df"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=35.00873>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temperature.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RwqXHZZBrcv",
        "outputId": "b4fa6fc8-ef8b-4e0f-cfe6-afdb17199577"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=35.0>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.math.reduce_std(temperature_data) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hT2EUMRCMgG",
        "outputId": "a8c01d57-c6c6-40d4-95b6-9fc84251d9c3"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=3.9290223>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temperature.stddev()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHhyfM8JCZlA",
        "outputId": "5d40a615-7a63-4316-e9b0-53ee777856bb"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=4.0>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Multivariate Normal"
      ],
      "metadata": {
        "id": "UK1YDBjyGAIU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather = tfd.MultivariateNormalDiag(loc = [35, 56], scale_diag=[4, 15])"
      ],
      "metadata": {
        "id": "YiqrwZaTCcAW"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weather_data = weather.sample(1000)"
      ],
      "metadata": {
        "id": "2ZEe_RWdGgQ0"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(weather_data[:, 0], weather_data[:, 1], color='blue', alpha=0.4)\n",
        "plt.xlabel(\"Temperature Degree Celsius\")\n",
        "plt.ylabel(\"Humidity %\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "nJvmTG_lGwey",
        "outputId": "133df653-bb41-4546-9770-9f9c9f034497"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Humidity %')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "knrj91c7G_IU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}